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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #314246

Title: Assessing corn water stress using spectral reflectance

Author
item DeJonge, Kendall
item MEFFORD, BRENNA - State Of Wyoming
item CHAVEZ, JOSE - Colorado State University

Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2016
Publication Date: 5/6/2016
Citation: DeJonge, K.C., Mefford, B.S., Chavez, J.L. 2016. Assessing corn water stress using spectral reflectance. International Journal of Remote Sensing. 37:10, 2294-2312, doi:10.1080/01431161.2016.1171929.

Interpretive Summary: Multiple remote sensing techniques have been developed to identify crop water stress, but some methods may be difficult for farmers to apply. If spectral reflectance data can be used to monitor crop water stress, growers could use this information as a quick low-cost guideline for irrigation management, thus helping save water by preventing over irrigating and achieving desired crop yields. Ground based multispectral data were collected and three different vegetation indices were evaluated; the Normalized Difference Vegetation Index (NDVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), and Green Normalized Difference Vegetation Index (GNDVI). The three vegetation indices were normalized and compared to water stress as indicated by the stress coefficient (Ks), and water deficit in the root zone calculated using a soil water balance. Validation results had acceptable R2 values (Nratio = 0.66, Gratio = 0.63, Oratio = 0.66), and RMSE values (Nratio = 0.043, Gratio = 0.036, Oratio = 0.043) between Ks and the vegetation indices.

Technical Abstract: Multiple remote sensing techniques have been developed to identify crop water stress, but some methods may be difficult for farmers to apply. If spectral reflectance data can be used to monitor crop water stress, growers could use this information as a quick low-cost guideline for irrigation management, thus helping save water by preventing over irrigating and achieving desired crop yields. Data collected in the 2013 growing season near Greeley, CO, where drip irrigation was used to irrigate twelve corn (Zea mays L.) treatments of varying water deficit levels. Ground based multispectral data were collected and three different vegetation indices were evaluated. These included the Normalized Difference Vegetation Index (NDVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), and Green Normalized Difference Vegetation Index (GNDVI). The three vegetation indices were compared to water stress as indicated by the stress coefficient (Ks), and water deficit in the root zone calculated using a soil water balance. To compare the indices to Ks, vegetation ratios were developed from vegetation indices in the process of normalization. Vegetation ratios are defined as the non-stressed vegetation index divided by the stressed vegetation index. Results showed that vegetation ratios were sensitive to water stress as indicated by good goodness of fit (R2) values (Nratio = 0.53, Gratio = 0.46, Oratio = 0.49) and low Root Mean Square Error (RMSE) values (Nratio = 0.076, Gratio = 0.062, Oratio = 0.076) when compared to Ks. In order to use spectral reflectance to manage crop water stress an irrigation trigger point of 0.93 for the vegetation ratios was determined. These results were validated using data collected from a different field. The performance of the vegetation ratio approach resulted better than when applied to the main field giving higher R2 values (Nratio = 0.66, Gratio = 0.63, Oratio = 0.66), and lower RMSE values (Nratio = 0.043, Gratio = 0.036, Oratio = 0.043) between Ks and the vegetation indices.